The Entrepreneur’s Choice of Location: Evidence from the Life Sciences Christos Kolympiris Peter G. Klein Nicholas Kalaitzandonakes Department of Agricultural Economics University of Missouri-Columbia Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Portland, OR, July 29-August 1, 2007 Corresponding author: Peter G. Klein, Department of Agricultural Economics, 135D Mumford Hall, Columbia, MO 65211, [email protected].
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The Entrepreneur’s Choice of Location: Evidence from the Life Sciences
Christos Kolympiris Peter G. Klein
Nicholas Kalaitzandonakes
Department of Agricultural Economics University of Missouri-Columbia
Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Portland, OR, July 29-August 1, 2007
Corresponding author: Peter G. Klein, Department of Agricultural Economics, 135D Mumford Hall, Columbia, MO 65211, [email protected].
The Entrepreneur’s Choice of Location: Evidence from the Life Sciences Abstract: Why do biotech firms cluster? New and established firms in biotech clusters are said to capitalize on knowledge spillovers, labor-market pooling, and other externalities. Some have even argued that such spillovers are so strong that the cluster itself, rather than the individual, is the “locus of entrepreneurship.” Such arguments, however, do not resolve the mechanism by which clusters might contribute to the establishment of new firms. This paper proposes a concep-tual framework for analyzing the locational choices of entrepreneurial firms in the life-sciences industry. Building on both the cluster literature and the literature on entrepreneurship, we de-velop hypotheses about how cluster characteristics, the entrepreneur’s personal characteristics, and characteristics of the business environment affect the entrepreneur’s decision to establish a new firm. We argue that a key factor in the location decision is the relative mobility of the ap-propriate resources. Our main hypothesis is that specialized labor is less mobile than capital and other resources and that it is the base from which entrepreneurs are ultimately created. If so, new firms will emerge in areas characterized by an existing concentration of specialized labor. This labor pool may be “spawned” by universities and incumbent small and larger biotech (or other high-technology) companies. An alternative explanation is that entrepreneurs establish new ven-tures outside the cluster, then move them to the cluster to take advantage of local knowledge and other resources. Or a potential entrepreneur could conceive a business plan, then relocate to an existing cluster before founding a firm. We explain how survey data can be used to sort through these explanations. JEL codes: L26, L65, O18, O32 Keywords: entrepreneurship, biotechnology, clusters, knowledge spillovers, agglomeration economies
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Much of the entrepreneurship literature over the last two decades focuses on the personal
characteristics of those individuals who become entrepreneurs. More recently, however, econo-
mists and management scholars have turned their attention to the manner in which entrepreneur-
ship is manifested. Under what circumstances, for example, do entrepreneurs establish firms to
realize their entrepreneurial visions? (See, for example, the papers collected in Alvarez and Bar-
ney) What critical resources—venture finance, human capital, infrastructure, intellectual prop-
erty—are necessary for entrepreneurial ventures to succeed? What explains why some communi-
ties, regions, and even nations are more “entrepreneurial” than others?
A substantial literature explores the entrepreneur’s choice to create a firm (see the review in
Shane, chapter 10), and several papers examine the way a new venture should be organized
(cites). Less attention has been paid, however, to the entrepreneur’s decision where to locate a
new venture. How important is location to the entrepreneurial firm? Should the entrepreneur lo-
cate close to other entrepreneurs or far from them? Which is better, a cluster of same-industry
firms or a more diverse environment with a mix of specialties, or a mix of new and established
firms? Are some clusters more desirable than others? Which is more important, proximity to
venture capitalists or proximity to cheap labor?
It is well known that new firms, particularly in knowledge-intensive, high-technology sec-
tors like software, pharmaceuticals, and biotechnology, tend to emerge in clusters like Califor-
nia’s Silicon Valley and Boston’s Route 128. However, the dynamics of cluster formation are
difficult to analyze. Do entrepreneurs make their location choices independently, with groups of
firms subsequently arbitrarily designated as “clusters,” or do entrepreneurs chose to locate near
groups of existing firms with shared characteristics? What common features are necessary to
classify firms as belonging to one cluster or another? Are clusters defined exclusively by geogra-
phy, or is temporal clustering (or clustering around some other dimension) equally important?
Despite substantial work in economic geography, industrial organization, labor economics,
and the management of innovation and technology on clusters, little is known about the location
decision from the entrepreneur’s point of view. Most of the literature takes the cluster as the unit
3
of analysis, asking why clusters form in particular locations and how cluster characteristics (size,
age, structure, growth rate, degree of similarity, and the like) affect the behavior and perform-
ance of cluster firms and the performance of the area or region. Less attention has been paid,
however, to the microfoundations of this process—the decisions of individual entrepreneurs to
locate in clusters. The entrepreneur’s choice of location and the emergence of the cluster itself
are endogenous and cannot be treated independently. On the other hand, from the entrepreneur’s
perspective the behavior of other entrepreneurs, the existence of anchor entities such as universi-
ties, research institutes, and established companies, and government policy toward entrepreneur-
ship in a particular region can be taken as given.
This paper suggests a conceptual framework for understanding the locational choices of en-
trepreneurial firms in the life-sciences industry. Strict regulation, a complex science, and fre-
quently ambiguous intellectual property rights differentiate biotechnology and the other life sci-
ences from most other high-technology sectors and prolong its R&D cycles. Despite lengthy re-
search and product cycles, however, a large share of the R&D work in the biotech sector has his-
torically been performed by entrepreneurial startup firms. According to the National Science
Foundation (NSF) out of the total number of firms performing biotechnology R&D 61, 94 and 93
percent of them employed less than 499 employees for the years 2001, 2002 and 2003 respec-
tively. Even though the connection between size and age of firms is not definite, the preceding
statistics indicate that small and typically startup firms do perform the majority of R&D in the
biotech industry. Moreover, biotech startups tend to cluster. Indeed, a handful of U.S. loca-
tions—Boston, Raleigh-Durham, San Diego, San Francisco, Seattle, and St. Louis—account for
a large share of the biotech firms established in the 1970s and 1980s and over half the startups
that were created in the last decade. For these reasons, the biotechnology sector provides an ex-
cellent setting for examining our hypotheses.
Why do biotech firms cluster? New and established firms in biotech clusters are said to capi-
talize on knowledge spillovers, labor-market pooling, and other externalities. Some have even
argued that such spillovers are so strong that the cluster itself, rather than the individual, is the
“locus of entrepreneurship.” Such arguments, however, do not resolve the mechanism by which
4
clusters might contribute to the establishment of new firms. In the paper we study at a theoretical
level the interactions between biotech entrepreneurs and clusters as well as the relative contribu-
tion of such interactions to entrepreneurial activity and success. In particular, our analysis fo-
cuses on the following questions: What cluster resources (e.g. research infrastructure, financing,
human capital pools, social capital, etc.) are most important in encouraging biotech entrepreneur-
ship? And, how do the structural characteristics of the cluster (e.g. size, scope, composition, exis-
tence of anchor firms, etc.) affect the likelihood of entrepreneurial activity?
Our investigation focuses on two related questions. First, why start a new firm? This question
has received considerable attention in the entrepreneurship literature. Using primary data (inter-
views, surveys, and experiments) and, to a lesser extent, secondary sources (such as the Panel
Study on Income Dynamics [PSID]), researchers have identified several individual-level charac-
teristics that are positively correlated with new firm formation. Second, given that an entrepre-
neur is committed to establishing a new venture, what determines the choice of location? Should
the entrepreneur locate in a cluster of same-industry firms, or a more diverse environment with a
mix of specialties, or a mix of new and established firms? Why choose one cluster over another?
Despite substantial work in economic geography and industrial organization on clusters, little is
known about this decision from the entrepreneur’s point of view. Building on both the cluster
literature and the literature on entrepreneurship, we develop hypotheses about how cluster char-
acteristics, the entrepreneur’s personal characteristics, and characteristics of the business envi-
ronment affect the entrepreneur’s decision to establish a new firm.
We argue that a key factor in the location decision is the relative mobility of the appropriate
resources. Our main hypothesis is that specialized labor is less mobile than capital and other re-
sources and that it is the base from which entrepreneurs are ultimately created. If so, new firms
will emerge in areas characterized by an existing concentration of specialized labor. This labor
pool may be “spawned” by universities and incumbent small and larger biotech (or other high-
technology) companies. An alternative explanation is that entrepreneurs establish new ventures
outside the cluster, then move them to the cluster to take advantage of local knowledge and other
5
resources. Or a potential entrepreneur could conceive a business plan, then relocate to an existing
cluster before founding a firm.
The paper proceeds as follows. We begin with a comprehensive review of the theoretical and
empirical literature on the entrepreneur’s choice of location, including cluster characteristics, the
entrepreneur’s individual characteristics, and characteristics of the general business environment.
Next we develop a set of hypotheses about the relationship between industry, firm, and entrepre-
neur characteristics and the spatial distribution of entrepreneurial firms. We conclude by discuss-
ing strategies for examining these hypotheses empirically.
Effects of Cluster Characteristics on Biotechnology Startups
Recent years have witnessed a resurgence of interest in the economics of location, particu-
larly as applied to economic growth, international trade, and regional development (see Fujita,
Krugman, and Venables, 1999, for an overview). Economists have long recognized (at least
since von Thünen) that transportation costs play an important role in the firm’s location decision
and subsequent profitability. The modern literature builds on Marshall’s (1890) concept of ag-
glomeration economies, the benefits from locating close to firms producing complementary or
substitute products and sharing key resources and markets. Marshall argued that industrial dis-
tricts emerge because of horizontal knowledge spillovers, specialized labor markets, and links to
vertically related firms. Recent contributions to cluster theory, such as Porter (1998), explain
cluster formation in terms of factor conditions, demand conditions, strategic considerations, the
presence of supporting industries, regulatory and tax incentives, and chance. Much of the recent
literature focuses on location-specific intangibles such as knowledge spillovers from horizontally
and vertically related firms; access to universities, incubators, and other sources of specialized
technical knowledge; and a general climate for innovation.
Agglomeration economies
The literature has identified two sources of agglomeration economies. The first, localization
economies, describes the gains from locating close to firms with similar characteristics (firms in
the same industry, firms sharing a common organizational or financial structure, firms of similar 6
age and size, or whatever). The second, urbanization economies, describes the benefits of variety
or heterogeneity—gains from complementary use of shared resources, experimentation with
novel strategies and forms of organization, and the like. While most of the recent literature on
agglomeration has focused on localization economies, the benefits from urbanization may be
equally important.
Localization economies exist when the gains from locating in a particular place are increas-
ing in the number of “similar” or “related” firms already in that place. Theories of localization
depend on the definitions of similar and related. As discussed below, these concepts are often
difficult to define precisely.
The literature on geographic concentration emphasizes the resource and vertical dimension of
the firms and cluster. “[I]ndividual firms, in aiming to minimize their observable spatial transac-
tions costs, have implicitly or explicitly determined that this is best achieved by locating close to
other firms within the particular input-output production and consumption hierarchy of which
they are part” (Gordon and McCann, 2000). This attraction of firms through vertical relation-
ships is evident in the biotechnology industry. Prevezer (1997) notes that “sophisticated [bio-
technology] buyers attract suppliers of product that the buyer needs. This is attraction via de-
mand for new products and is likely to have been a significant force in the creation of new
equipment and research tools companies.”
Access to an adequate supply of upstream and downstream firms in the local region provides
opportunities for several externalities. Local suppliers encourage close contact and opportunities
for cooperation and negotiations to reduce costs and ensure quality and reliability in the vertical
relationship (Tallman et al., 2004). Unconventional linkages and industries also play a significant
role in the biotech and biomedical industry clusters. Prevezer describes this with importance of
related firms in close proximity that employ similar core technologies (1997). However, the
presence of these features does not ensure the externalities are achieved as Gordon and McCann
describe (2000). Thus, the location of firms and industries provide an indicator of the opportunity
for supply and demand externalities. Also, these types of connections are believed to decrease
costs when they are engaged within the local cluster (Dalpe, 2003). 7
The most important spillovers, whether horizontal or vertical, may be less easily observed.
Indeed, the literature on clusters and regional development (Kogut and Zander, 1992; Jaffe, Tra-
emphasize the importance of infrastructure for biomedical and biotechnology firms in Ohio and
Sweden; Dalpe (2003) also highlights the importance of infrastructure for small biotech firms
(2003).
As mentioned above, a specialized labor force is one of Marshall’s three determinants of re-
gional concentration (Fingleton, Igliori and Moore, 2003). Complementary industries may often
rely on the same core workforce, and consequently co-locate to take advantage of this shared re-
source. Krugman (1991) argues that risk and increasing returns create a labor-market network
effect; the larger the labor market and the network of firms using it, the greater the benefits for
the individual employer. Labor pooling externalities may include reduced search costs for firms
and individuals, diffusion of technical knowledge among organizations with the rearrangement
of employees among organizations (Glaeser, 2000), a rich supply of skilled labor (Glaeser, 2000;
Krugman, 1991), productivity gains, and the like.
Besides physical resources and infrastructure, labor markets, and other inputs, the presence
of particular firms or organizations, known as “anchors,” can also affect the entrepreneur’s loca-
tion choice. Feldman (2002) argues that “existing firms may serve as anchors that establish
skilled labor pools, specialized intermediate industries and provide knowledge spillovers for new
technology intensive firms in the region” (see also Audretsch and Feldman, 1996). Universities,
nonprofit research institutions and foundations, or incubators can also serve as anchors (cites).
Incubators are particularly important for new life-sciences firms (Cetindamar and Laage-
Hellman, 2003).
Several papers also examine the relationship between the legal, regulatory, and political cli-
mate and general levels of entrepreneurial activity. Kreft and Sobel, for example, use state-level 17
data on startups, venture capital, and the Fraser Institute Index of Economic Freedom to show
that states with lower tax burdens, modest and transparent regulatory regimes, and protection for
property rights attract more venture capital which, in turn, spurs entrepreneurial activity. Com-
prehensive data on these aspects of the business environment are difficult to obtain at the city or
county level, but could be assessed from survey data on respondents’ subjective perceptions of
legal, regulatory, and political constraints.
Hypotheses and Strategies for Empirical Analysis
The foregoing discussion suggests several testable hypotheses about the location of entrepre-
neurial firms in the life sciences industry. Our reading of the literature suggests the following
general model of location:
p (entrepreneur i starts a firm in cluster j) = f(C, E, X), where C is a vector of
cluster characteristics, E a vector of entrepreneur characteristics, and X a vector
of business environment characteristics and control variables).
The vector C includes measures of urbanization economies (+), localization economies (+),
product differentiation (+), and the entrepreneur’s expected position in cluster. The vector E in-
cludes prior entrepreneurial experience (in any sector) (+), prior life-sciences employment ex-
perience) (+), education (+), age (U-shaped), wealth (+), self-employed parent (+), social status
(+), and social ties (+). The vector X includes access to specialized resource (+), transportation
costs (−), the availability of venture capital (+), and measures of institutional characteristics such
as economic freedom (+).
One strategy for empirical implementation of this model is to collect data from founding en-
trepreneurs about their ventures, where their ventures were founded, and their perceptions about
the strength of various cluster, entrepreneur, and business environment characteristics in the de-
cision to establish a new venture. Our analysis suggests the following potential hypotheses:
Proposition 1: Entrepreneurs are more likely to start firms in the presence of pre-
viously existing agglomeration economies (economies of urbanization or localiza-
tion) 18
The importance of agglomeration economies is one of the best established results in the exist-
ing literature. The problem is the direction of causality; do agglomeration economies, once estab-
lished, attract additional firms, or do firms come into existence in particular locations for other
reasons, leading to agglomeration economies ex post? By measuring the founding entrepreneur’s
perception of the role of agglomeration economies before choosing to locate in the cluster (by
founding a new firm in the cluster or moving an existing firm to the cluster) we can avoid the
endogeneity problems that plague studies based on secondary data.
Both economies of localization and economies of urbanization are viewed in the literature as
important. The relationship between the relative strength of these factors and industry age is not
established clearly, so we cannot say, a priori, which effect is likely to dominate in an emerging
industry like biotechnology. Because nascent industries like biotech are unable to draw on local-
ized pools of highly specialized labor, but must rely on more heterogeneous pools of workers
with general skills, we conjecture that the urbanization effect outweighs the localization effect,
suggesting Proposition 2:
Proposition 2: Urbanization economies will have a greater impact than localiza-
tion economies in the firm founding decision in biotechnology.
The literature on market structure discussed above suggests that firms prefer to avoid intense
price competition, leading to Proposition 3:
Proposition 3: Price competition in the target market lowers the probability of
starting a new venture in that market.
Entrepreneurs pay attention not only to the existence of clusters, but the cluster density,
structure and the entrepreneur’s expected position within the cluster. The discussion above sug-
gests a nonlinear relationship between cluster density and attractiveness to new firms. From this
we derive an additional proposition:
Proposition 4: The effect of cluster density on the probability an entrepreneur will
locate in a particular cluster is increasing up to a threshold level and decreasing
afterwards.
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To examine these propositions, we are designing a survey of founding entrepreneurs in life-
sciences companies, both inside and outside the major clusters located throughout the U.S. The
main variable of interest is the role of locational factors in the entrepreneur’s start-up decision.
We will ask our sample entrepreneurs if they started their firms where they already lived and
worked, if they started firms elsewhere before moving to their current location, or if they moved
to their current location specifically to start a new life-sciences firm. We will also ask about the
entrepreneur’s personal characteristics (age, education, wealth, prior entrepreneurial experience,
prior employment experience) and the entrepreneur’s perception of the business environment
factors that have contributed to their entrepreneurial success (ease of access to specialized physi-
cal, labor, and financial resources; access to relevant personal/social networks, etc.). We will
combine the survey data with secondary data on firm and regional characteristics to provide
comprehensive cross-section of the nascent life-sciences industry.
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